Cargando…
An Integrative Genomic Prediction Approach for Predicting Buffalo Milk Traits by Incorporating Related Cattle QTLs
Background: The 90K Axiom Buffalo SNP Array is expected to improve and speed up various genomic analyses for the buffalo (Bubalus bubalis). Genomic prediction is an effective approach in animal breeding to improve selection and reduce costs. As buffalo genome research is lagging behind that of the c...
Autores principales: | , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9408041/ https://www.ncbi.nlm.nih.gov/pubmed/36011341 http://dx.doi.org/10.3390/genes13081430 |
_version_ | 1784774510157758464 |
---|---|
author | Hao, Xingjie Liang, Aixin Plastow, Graham Zhang, Chunyan Wang, Zhiquan Liu, Jiajia Salzano, Angela Gasparrini, Bianca Campanile, Giuseppe Zhang, Shujun Yang, Liguo |
author_facet | Hao, Xingjie Liang, Aixin Plastow, Graham Zhang, Chunyan Wang, Zhiquan Liu, Jiajia Salzano, Angela Gasparrini, Bianca Campanile, Giuseppe Zhang, Shujun Yang, Liguo |
author_sort | Hao, Xingjie |
collection | PubMed |
description | Background: The 90K Axiom Buffalo SNP Array is expected to improve and speed up various genomic analyses for the buffalo (Bubalus bubalis). Genomic prediction is an effective approach in animal breeding to improve selection and reduce costs. As buffalo genome research is lagging behind that of the cow and production records are also limited, genomic prediction performance will be relatively poor. To improve the genomic prediction in buffalo, we introduced a new approach (pGBLUP) for genomic prediction of six buffalo milk traits by incorporating QTL information from the cattle milk traits in order to help improve the prediction performance for buffalo. Results: In simulations, the pGBLUP could outperform BayesR and the GBLUP if the prior biological information (i.e., the known causal loci) was appropriate; otherwise, it performed slightly worse than BayesR and equal to or better than the GBLUP. In real data, the heritability of the buffalo genomic region corresponding to the cattle milk trait QTLs was enriched (fold of enrichment > 1) in four buffalo milk traits (FY270, MY270, PY270, and PM) when the EBV was used as the response variable. The DEBV as the response variable yielded more reliable genomic predictions than the traditional EBV, as has been shown by previous research. The performance of the three approaches (GBLUP, BayesR, and pGBLUP) did not vary greatly in this study, probably due to the limited sample size, incomplete prior biological information, and less artificial selection in buffalo. Conclusions: To our knowledge, this study is the first to apply genomic prediction to buffalo by incorporating prior biological information. The genomic prediction of buffalo traits can be further improved with a larger sample size, higher-density SNP chips, and more precise prior biological information. |
format | Online Article Text |
id | pubmed-9408041 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94080412022-08-26 An Integrative Genomic Prediction Approach for Predicting Buffalo Milk Traits by Incorporating Related Cattle QTLs Hao, Xingjie Liang, Aixin Plastow, Graham Zhang, Chunyan Wang, Zhiquan Liu, Jiajia Salzano, Angela Gasparrini, Bianca Campanile, Giuseppe Zhang, Shujun Yang, Liguo Genes (Basel) Article Background: The 90K Axiom Buffalo SNP Array is expected to improve and speed up various genomic analyses for the buffalo (Bubalus bubalis). Genomic prediction is an effective approach in animal breeding to improve selection and reduce costs. As buffalo genome research is lagging behind that of the cow and production records are also limited, genomic prediction performance will be relatively poor. To improve the genomic prediction in buffalo, we introduced a new approach (pGBLUP) for genomic prediction of six buffalo milk traits by incorporating QTL information from the cattle milk traits in order to help improve the prediction performance for buffalo. Results: In simulations, the pGBLUP could outperform BayesR and the GBLUP if the prior biological information (i.e., the known causal loci) was appropriate; otherwise, it performed slightly worse than BayesR and equal to or better than the GBLUP. In real data, the heritability of the buffalo genomic region corresponding to the cattle milk trait QTLs was enriched (fold of enrichment > 1) in four buffalo milk traits (FY270, MY270, PY270, and PM) when the EBV was used as the response variable. The DEBV as the response variable yielded more reliable genomic predictions than the traditional EBV, as has been shown by previous research. The performance of the three approaches (GBLUP, BayesR, and pGBLUP) did not vary greatly in this study, probably due to the limited sample size, incomplete prior biological information, and less artificial selection in buffalo. Conclusions: To our knowledge, this study is the first to apply genomic prediction to buffalo by incorporating prior biological information. The genomic prediction of buffalo traits can be further improved with a larger sample size, higher-density SNP chips, and more precise prior biological information. MDPI 2022-08-11 /pmc/articles/PMC9408041/ /pubmed/36011341 http://dx.doi.org/10.3390/genes13081430 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Hao, Xingjie Liang, Aixin Plastow, Graham Zhang, Chunyan Wang, Zhiquan Liu, Jiajia Salzano, Angela Gasparrini, Bianca Campanile, Giuseppe Zhang, Shujun Yang, Liguo An Integrative Genomic Prediction Approach for Predicting Buffalo Milk Traits by Incorporating Related Cattle QTLs |
title | An Integrative Genomic Prediction Approach for Predicting Buffalo Milk Traits by Incorporating Related Cattle QTLs |
title_full | An Integrative Genomic Prediction Approach for Predicting Buffalo Milk Traits by Incorporating Related Cattle QTLs |
title_fullStr | An Integrative Genomic Prediction Approach for Predicting Buffalo Milk Traits by Incorporating Related Cattle QTLs |
title_full_unstemmed | An Integrative Genomic Prediction Approach for Predicting Buffalo Milk Traits by Incorporating Related Cattle QTLs |
title_short | An Integrative Genomic Prediction Approach for Predicting Buffalo Milk Traits by Incorporating Related Cattle QTLs |
title_sort | integrative genomic prediction approach for predicting buffalo milk traits by incorporating related cattle qtls |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9408041/ https://www.ncbi.nlm.nih.gov/pubmed/36011341 http://dx.doi.org/10.3390/genes13081430 |
work_keys_str_mv | AT haoxingjie anintegrativegenomicpredictionapproachforpredictingbuffalomilktraitsbyincorporatingrelatedcattleqtls AT liangaixin anintegrativegenomicpredictionapproachforpredictingbuffalomilktraitsbyincorporatingrelatedcattleqtls AT plastowgraham anintegrativegenomicpredictionapproachforpredictingbuffalomilktraitsbyincorporatingrelatedcattleqtls AT zhangchunyan anintegrativegenomicpredictionapproachforpredictingbuffalomilktraitsbyincorporatingrelatedcattleqtls AT wangzhiquan anintegrativegenomicpredictionapproachforpredictingbuffalomilktraitsbyincorporatingrelatedcattleqtls AT liujiajia anintegrativegenomicpredictionapproachforpredictingbuffalomilktraitsbyincorporatingrelatedcattleqtls AT salzanoangela anintegrativegenomicpredictionapproachforpredictingbuffalomilktraitsbyincorporatingrelatedcattleqtls AT gasparrinibianca anintegrativegenomicpredictionapproachforpredictingbuffalomilktraitsbyincorporatingrelatedcattleqtls AT campanilegiuseppe anintegrativegenomicpredictionapproachforpredictingbuffalomilktraitsbyincorporatingrelatedcattleqtls AT zhangshujun anintegrativegenomicpredictionapproachforpredictingbuffalomilktraitsbyincorporatingrelatedcattleqtls AT yangliguo anintegrativegenomicpredictionapproachforpredictingbuffalomilktraitsbyincorporatingrelatedcattleqtls AT haoxingjie integrativegenomicpredictionapproachforpredictingbuffalomilktraitsbyincorporatingrelatedcattleqtls AT liangaixin integrativegenomicpredictionapproachforpredictingbuffalomilktraitsbyincorporatingrelatedcattleqtls AT plastowgraham integrativegenomicpredictionapproachforpredictingbuffalomilktraitsbyincorporatingrelatedcattleqtls AT zhangchunyan integrativegenomicpredictionapproachforpredictingbuffalomilktraitsbyincorporatingrelatedcattleqtls AT wangzhiquan integrativegenomicpredictionapproachforpredictingbuffalomilktraitsbyincorporatingrelatedcattleqtls AT liujiajia integrativegenomicpredictionapproachforpredictingbuffalomilktraitsbyincorporatingrelatedcattleqtls AT salzanoangela integrativegenomicpredictionapproachforpredictingbuffalomilktraitsbyincorporatingrelatedcattleqtls AT gasparrinibianca integrativegenomicpredictionapproachforpredictingbuffalomilktraitsbyincorporatingrelatedcattleqtls AT campanilegiuseppe integrativegenomicpredictionapproachforpredictingbuffalomilktraitsbyincorporatingrelatedcattleqtls AT zhangshujun integrativegenomicpredictionapproachforpredictingbuffalomilktraitsbyincorporatingrelatedcattleqtls AT yangliguo integrativegenomicpredictionapproachforpredictingbuffalomilktraitsbyincorporatingrelatedcattleqtls |